Multiuser detection (“MUD”) is an effective approach for detecting multiple, simultaneous data streams transmitted by a plurality of frequency sources (generically referred to herein as “users”) on a common frequency channel, thereby providing a significant increase in the spectral efficiency of the communication network. Applications include, but are not limited to, cellular telephone communication of voice and data to and from cellular handsets, other wireless mobile devices, and wireless base stations.
Often, it is possible during multiuser detection to accurately decode at least some of the data streams included in a received transmission, so that checksums or other accuracy verification tests are met. Nevertheless, other data streams included in the transmission typically remain that cannot be decoded with complete accuracy, due for example to noise and interference from unwanted signals. Accordingly, the data streams that cannot be accurately decoded must be approximated, or “modeled,” so as to obtain a best possible estimate of the symbols transmitted in those data streams. The final result of multiuser detection is therefore typically a combination of “decoded” data streams, for which the transmitted symbols are accurately known, and “modeled” data streams, for which only best guess estimates of the transmitted symbols are known.
Several approaches have been proposed for modeling data streams in multiuser detection wireless communication networks that cannot be accurately decoded, including but not limited to minimum mean-square error (“MMSE”), zero forcing (“ZF”), maximum-likelihood detection (“MLD”), and many others. The MLD approach is optimal, but has a very high implementation complexity for even moderate numbers of undecoded users. As an alternative, linear equalization methods such as multiuser MMSE combined with interference rejection and sample matrix inversion are popular, due to their lower implementation complexity.
In particular, non-linear successive interference cancellation (“SIC”) receiver architectures involving turbo loops have recently become very attractive, because they provide performance that approaches the optimal performance of MLD detection, but have much lower implementation complexity. However, these methods can suffer from accumulation of estimation errors as successive turbo loops attempt to model additional users based on the inherently faulty assumption that the users modeled in previous loops have been modeled with complete accuracy.
What is needed, therefore, is an improved receiver design for modeling users in SIC turbo loop multiuser detection architectures, while minimizing accumulation of estimation errors in successive turbo loops.